Salinization of critical groundwater reserves in coastal Mediterranean areas: Identification, Risk Assessment, and Sustainable Management with the use of integrated modeling and smart ICT tools

The MEDSAL Project aims to secure the availability and quality of groundwater reserves in Mediterranean coastal areas, which are amongst the most vulnerable regions in the world to water scarcity and quality degradation.


MEDSAL aims to provide a novel holistic approach, towards the sustainable management of coastal aquifers, which are affected by increased (single or multi-source) groundwater salinization (GWS) risk, especially under the variable meteo-climatic conditions of the Mediterranean and the rapidly changing socio-economic context.

It is evident that salinization limits and menaces the availability of groundwater resources in the most populated and productive coastal areas of the Mediterranean. As a phenomenon, the salinization of coastal aquifers is a complex process often related to multiple causes such as lack of internal drainage, seawater intrusion, increased evaporation of water-logged areas, up-coning of deep-brines by over-abstraction, geogenic factors (e.g. evaporite dissolution, etc) and pollution.

The MEDSAL project team aims at improving the identification, detection, classification, modeling and risk assessment of salinization in the Mediterranean.

Identification and detection

MEDSAL aims at improving the speed, scope and precision of detecting salinization and by providing an integrated set of monitoring and prediction tools that capture the dynamics and risks of salinization.


MEDSAL will provide a classification of groundwater salinization types for Mediterranean coasts and innovative methods to detect these types, also in complex karstic and data-scarce environments.


These outcomes will be reached by better integration of hydrogeochemical and environmental isotope data with physical-based groundwater flow and transport models. Artificial intelligence and deep learning methods will be used to improve the detection of patterns in multi-dimensional hydrogeochemical and isotope data.